from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-24 14:10:42.720185
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 24, Dec, 2020
Time: 14:10:46
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.0284
Nobs: 150.000 HQIC: -45.1010
Log likelihood: 1612.05 FPE: 1.24440e-20
AIC: -45.8348 Det(Omega_mle): 6.96150e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.473344 0.163846 2.889 0.004
L1.Burgenland 0.141937 0.082576 1.719 0.086
L1.Kärnten -0.236363 0.066414 -3.559 0.000
L1.Niederösterreich 0.118598 0.192002 0.618 0.537
L1.Oberösterreich 0.247795 0.164010 1.511 0.131
L1.Salzburg 0.177554 0.085068 2.087 0.037
L1.Steiermark 0.074987 0.118666 0.632 0.527
L1.Tirol 0.151211 0.078517 1.926 0.054
L1.Vorarlberg 0.004570 0.076685 0.060 0.952
L1.Wien -0.135229 0.158986 -0.851 0.395
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.523620 0.213762 2.450 0.014
L1.Burgenland 0.010074 0.107732 0.094 0.926
L1.Kärnten 0.361494 0.086646 4.172 0.000
L1.Niederösterreich 0.104689 0.250495 0.418 0.676
L1.Oberösterreich -0.183344 0.213975 -0.857 0.392
L1.Salzburg 0.195980 0.110984 1.766 0.077
L1.Steiermark 0.249682 0.154818 1.613 0.107
L1.Tirol 0.142880 0.102437 1.395 0.163
L1.Vorarlberg 0.187587 0.100047 1.875 0.061
L1.Wien -0.574934 0.207421 -2.772 0.006
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.293503 0.071367 4.113 0.000
L1.Burgenland 0.106609 0.035968 2.964 0.003
L1.Kärnten -0.026211 0.028928 -0.906 0.365
L1.Niederösterreich 0.063297 0.083631 0.757 0.449
L1.Oberösterreich 0.290135 0.071438 4.061 0.000
L1.Salzburg -0.002507 0.037053 -0.068 0.946
L1.Steiermark -0.018339 0.051688 -0.355 0.723
L1.Tirol 0.089062 0.034200 2.604 0.009
L1.Vorarlberg 0.132061 0.033402 3.954 0.000
L1.Wien 0.079330 0.069250 1.146 0.252
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.189319 0.082329 2.300 0.021
L1.Burgenland -0.009246 0.041492 -0.223 0.824
L1.Kärnten 0.021819 0.033371 0.654 0.513
L1.Niederösterreich 0.022967 0.096476 0.238 0.812
L1.Oberösterreich 0.410759 0.082411 4.984 0.000
L1.Salzburg 0.097902 0.042744 2.290 0.022
L1.Steiermark 0.190091 0.059627 3.188 0.001
L1.Tirol 0.032196 0.039453 0.816 0.414
L1.Vorarlberg 0.101280 0.038532 2.628 0.009
L1.Wien -0.058565 0.079886 -0.733 0.463
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.571195 0.172939 3.303 0.001
L1.Burgenland 0.073558 0.087158 0.844 0.399
L1.Kärnten 0.008612 0.070099 0.123 0.902
L1.Niederösterreich -0.040450 0.202657 -0.200 0.842
L1.Oberösterreich 0.149516 0.173111 0.864 0.388
L1.Salzburg 0.050125 0.089789 0.558 0.577
L1.Steiermark 0.124191 0.125252 0.992 0.321
L1.Tirol 0.215335 0.082874 2.598 0.009
L1.Vorarlberg 0.014956 0.080941 0.185 0.853
L1.Wien -0.151179 0.167809 -0.901 0.368
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.172625 0.119361 1.446 0.148
L1.Burgenland -0.033697 0.060156 -0.560 0.575
L1.Kärnten -0.015784 0.048382 -0.326 0.744
L1.Niederösterreich 0.164978 0.139873 1.179 0.238
L1.Oberösterreich 0.409286 0.119480 3.426 0.001
L1.Salzburg -0.025392 0.061972 -0.410 0.682
L1.Steiermark -0.049083 0.086448 -0.568 0.570
L1.Tirol 0.191020 0.057199 3.340 0.001
L1.Vorarlberg 0.035513 0.055865 0.636 0.525
L1.Wien 0.160369 0.115821 1.385 0.166
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.213070 0.150169 1.419 0.156
L1.Burgenland 0.078135 0.075683 1.032 0.302
L1.Kärnten -0.045182 0.060870 -0.742 0.458
L1.Niederösterreich -0.054246 0.175974 -0.308 0.758
L1.Oberösterreich -0.118327 0.150319 -0.787 0.431
L1.Salzburg 0.009138 0.077967 0.117 0.907
L1.Steiermark 0.391973 0.108761 3.604 0.000
L1.Tirol 0.518646 0.071963 7.207 0.000
L1.Vorarlberg 0.225516 0.070284 3.209 0.001
L1.Wien -0.216450 0.145715 -1.485 0.137
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.124053 0.174729 0.710 0.478
L1.Burgenland 0.028383 0.088060 0.322 0.747
L1.Kärnten -0.114567 0.070825 -1.618 0.106
L1.Niederösterreich 0.213286 0.204754 1.042 0.298
L1.Oberösterreich 0.003863 0.174903 0.022 0.982
L1.Salzburg 0.225323 0.090718 2.484 0.013
L1.Steiermark 0.131586 0.126548 1.040 0.298
L1.Tirol 0.094066 0.083732 1.123 0.261
L1.Vorarlberg 0.034312 0.081779 0.420 0.675
L1.Wien 0.271974 0.169546 1.604 0.109
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.581159 0.096580 6.017 0.000
L1.Burgenland -0.018620 0.048675 -0.383 0.702
L1.Kärnten 0.000922 0.039148 0.024 0.981
L1.Niederösterreich -0.023693 0.113177 -0.209 0.834
L1.Oberösterreich 0.283498 0.096677 2.932 0.003
L1.Salzburg 0.011909 0.050144 0.237 0.812
L1.Steiermark 0.003937 0.069949 0.056 0.955
L1.Tirol 0.076537 0.046283 1.654 0.098
L1.Vorarlberg 0.180545 0.045203 3.994 0.000
L1.Wien -0.090903 0.093716 -0.970 0.332
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.129568 -0.021715 0.197825 0.239827 0.038367 0.088049 -0.109259 0.152557
Kärnten 0.129568 1.000000 -0.017067 0.180583 0.130643 -0.158339 0.165939 0.017587 0.292912
Niederösterreich -0.021715 -0.017067 1.000000 0.245287 0.071160 0.185501 0.081845 0.018343 0.337096
Oberösterreich 0.197825 0.180583 0.245287 1.000000 0.271198 0.278113 0.088170 0.062774 0.082868
Salzburg 0.239827 0.130643 0.071160 0.271198 1.000000 0.139873 0.053824 0.072227 -0.039836
Steiermark 0.038367 -0.158339 0.185501 0.278113 0.139873 1.000000 0.094143 0.071524 -0.160038
Tirol 0.088049 0.165939 0.081845 0.088170 0.053824 0.094143 1.000000 0.127235 0.117935
Vorarlberg -0.109259 0.017587 0.018343 0.062774 0.072227 0.071524 0.127235 1.000000 0.078880
Wien 0.152557 0.292912 0.337096 0.082868 -0.039836 -0.160038 0.117935 0.078880 1.000000